High-addressable error-diffusion rendering can be used to improve the overall image-quality of scanned documents. High-addressable error-diffusion is a “gray-to-binary” rendering technique that takes advantage of the finer output resolution (write) capabilities of the laser in order to improve the overall image-quality of the rendered output document. The high-addressable algorithm has traditionally used linear interpolation between two successive pixels to generate sub-pixel levels that effectively improve the edge-content of the binarized output. However, a dichotomy exists relative to the optimum interpolation techniques that should be used when processing so-called “perfect” pixel edge-transitions (where nearest-neighbor interpolation is desired) while at the same time processing image regions within the same document that contain smooth/gradual gray video transitions (where linear interpolation is more appropriate). Utilizing linear interpolation effectively blurs perfect edges (0→255/255→0) which results in diffusing a small error term to neighboring pixels. This generates undesirable local (edge) sub-pixels along a hard edge in an image. This has a tendency to blur high transition edges.
Unfortunately, in areas of “ideal” edge transitions, undesirable local “stray” sub-pixels are generated. This tends to degrade the quality of printed documents. For error diffusion image rendering, this edge-degradation has been known to be caused by distributing/diffusing an averaged video error component to its downstream neighborhood pixels. Although video averaging is desirable in scanned documents with mixed content where edge-transitions are less abrupt due to the scanner's modulation-transfer-function (MTF), it tends to cause problems when processing “perfect” edges that are typically encountered within synthetic (i.e. print-path) input images.
Error diffusion is a well-known rendering technique that maintains the gray integrity of the input image by “diffusing” an error component to its downstream neighborhood pixels after video thresholding. In a high-resolution extension to an error diffusion algorithm, intermediate sub-pixels are generated around video edge transitions by using linear interpolation between two successive pixels. This takes advantage of the high-resolution marking capability of the laser (IOT) that generally improves the image-quality of the rendered output.
In a traditional (Floyd & Steinberg) error diffusion algorithm, the generated error component that is diffused to the downstream neighboring pixels is calculated as the difference between the “Desired” and “Printed” values. In this case, the “Desired” value is simply the input gray pixel level, where the “Printed” value is either 255 or 0 (corresponding to an 8-bit input image). For high-addressabilities greater than unity, a pixel's “Printed” value is derived from the number of binary output sub-pixels that are generated, where the “Desired” value is the average gray level between two successive pixels.
The high-addressable error can be calculated as follows:Error=(Pixel(N)+Pixel(N+1))/2−(PrintedSubpixels*255)/(TotalSubpixels);TotalSubpixels=1 for 1× high-addressablity. TotalSubpixels=2 for 2× high-addressablity. TotalSubpixels=3 for 3× high-addressablity, and so on.
One way to visualize this error calculation would be to define the area of one binary output pixel to be equivalent to the maximum input gray pixel level (i.e. 255 for an 8-bit image). For high-addressabilities equal to one (i.e. HA=1), generating a “1” after thresholding would consume most of the marked-pixel area with 100% toner. Generating a 0 would produce a 0% fill (“white”). Although high-addressable error-diffusion provides the ability to reduce jagged edges on text and line-art, a problem exists in regions where so-called “perfect” edges are encountered. FIG. 6 illustrates a first video edge transition profile depicting a “perfect” edge, and a second video edge transition profile depicting a “scanned” edge typically encountered in scanned images.
FIG. 7 illustrates a binary input image (2A) and a binary output starburst input image (2B) rendered via a 2× high-addressable error diffusion method (HA=2×) using a bit-constrained software model. In this case, high-addressability is applied in the fast-scan (horizontal direction) only. Note the sporadic placement of sub-pixels on either side of the vertically-oriented lines of output image 2B relative to the 8-bit gray input image 2A.
FIG. 8 illustrates the error component corresponding to the high-addressable rendered output of image 2B. The edge-degradation (observed in image 2B) is the result of the non-zero error being diffused to its neighbor pixels. This error diffusion creates black/white sub-pixels on either side of the vertically-oriented lines. Ideally, the error that should be propagated to the downstream pixels would be zero since the lines only switch between 0 and 255 within the 8-bit gray input image.
What is needed in this art are increasingly sophisticated systems and methods for edge transition detection to improve print quality when rendering via high addressable error diffusion in an image processing environment which does not generate undesirable sub-pixels along a hard edge of the image.